In recent years, cationic amphipathic antimicrobial peptides (AMPs) have shown great promise in combating antibiotic resistance on account of their distinctive membrane-disruptive mechanism. However, the clinical application of AMPs is restricted by their unsatisfactory stability and safety. Although attempts have been made to improve the stability and safety of AMPs, many of them are accompanied by a decline in their antimicrobial activity and bacterial selectivity. To develop AMPs with excellent and balanced antimicrobial activity, stability, and safety using a combination strategy. A series of sC184b-derived peptide analogues were designed by a combination strategy of subtly adjusting the charges, hydrophobic properties, and introducing specific unnatural amino acids in a well-balanced manner. The antimicrobial activity, cytotoxicity, hemolytic activity, stability, anti-biofilm activity, mechanism of action, synergistic effects, in vivo efficacy, and pharmacokinetics of the analogues were evaluated. Among these analogues, P-α-02-B stood out for its broad-spectrum and potent antimicrobial activity, anti-biofilm activity, desirable bacterial selectivity, high plasma stability, and synergistic effect with antibiotic levofloxacin. P-α-02-B exhibited strong membrane disturbance effect, which could be explained by its rigid α-helical structure revealed by molecular dynamics simulations. More importantly, P-α-02-B showed favorable therapeutic efficacy in vivo, whether used alone or in combination with levofloxacin. P-α-02-B is a promising antimicrobial agent for MDR bacterial infections, demonstrating the effectiveness of the combination strategy for AMP development.
According to the change of impact angle at the flight terminal of missile.This paper introduces an optimal control theory to find the way to control the impact angle.The impact-angle-control guidance law in three-dimensional space is studied.Using the maximum principle and method of solving the Riccati equation of optimal control theory helps to give two kinds of impact angle control guidance law refered to diving plane and horizontal plane.The simulation of several engagement situations proves its validity and feasibility,which demonstrates that the new guidance law can be used to hit a target at a desirable impact angle.
In this paper, a simple end-to-end obstacle avoidance method is introded for manipulators. First of all, the 2D images in the workspace are used to uniformly describe the characteristics of obstacles in the 3D space. After that, a model-free reinforcement learning algorithm DrQ-v2 is used to train the obstacle avoidance strategy, which directly outputs the joint angles to avoid the obstacles autonomously and accurately in the joint space. Finally, the simulation results demonstrate that the proposed end-to-end simple control method is more effective and convenient in handling obstacle avoidance tasks in complex dynamic environments.
Ballistic target recognition is of great significance for space attack and defense. The micro-motion features, which contain spatial and motion information, can be regarded as the foundation of the recognition of ballistic targets. To take full advantage of the micro-motion information of ballistic targets, this paper proposes a method based on feature fusion to recognize ballistic targets. The proposed method takes two types of data as input: the time–range (TR) map and the time–frequency (TF) spectrum. An improved feature extraction module based on 1D convolution and time self-attention is applied first to extract the multi-level features at each time instant and the global temporal information. Then, to efficiently fuse the features extracted from the TR map and TF spectrum, deep generalized canonical correlation analysis with center loss (DGCCA-CL) is proposed to transform the extracted features into a hidden space. The proposed DGCCA-CL possesses better performance in two aspects: small intra-class distance and compact representation, which is crucial to the fusion of multi-modality data. At last, the attention mechanism-based classifier which can adaptively focus on the important features is employed to give the target types. Experiment results show that the proposed method outperforms other network-based recognition methods.
Under the action of shock wave due to non-contact underwater explosion,ships with small size or large stiffness will have the response of large rigid-body motion.Based on Taylor plate equation,and taking the additional mass of transverse motion into account,the kinematic equations of 2D cross-section under the shock wave action due to the non-contact underwater explosion were established.And the response of the rigid body motion of a ship to the explosion was calculated.The results are in good agreement with the experimental data,which shows that the present method can be applied in prediction of motion response of ships under shock wave action.
This letter investigates the micromotion target detection problem for the multichannel synthetic aperture radar (SAR)- ground moving target indication system. The multichannel SAR signal models of the micromotion target and the ground clutter in the raw data domain are established firstly. Then the generalized likelihood ratio test (GLRT) of the micromotion target is derived. Based on the analysis of the probability density functions of the test statistics, theoretical detection performance dependent on the micromotion parameters is provided. Simulated heterogeneous SAR data validate the effectiveness of the GLRT detector.
Factorization Machine (FM) is a supervised learning approach with a powerful capability of feature engineering. It yields state-of-the-art performance in various batch learning tasks where all the training data is made available prior to the training. However, in real-world applications where the data arrives sequentially in a streaming manner, the high cost of re-training with batch learning algorithms has posed formidable challenges in the online learning scenario. The initial challenge is that no prior formulations of FM could fulfill the requirements in Online Convex Optimization (OCO) -- the paramount framework for online learning algorithm design. To address the aforementioned challenge, we invent a new convexification scheme leading to a Compact Convexified FM (CCFM) that seamlessly meets the requirements in OCO. However for learning Compact Convexified FM (CCFM) in the online learning setting, most existing algorithms suffer from expensive projection operations. To address this subsequent challenge, we follow the general projection-free algorithmic framework of Online Conditional Gradient and propose an Online Compact Convex Factorization Machine (OCCFM) algorithm that eschews the projection operation with efficient linear optimization steps. In support of the proposed OCCFM in terms of its theoretical foundation, we prove that the developed algorithm achieves a sub-linear regret bound. To evaluate the empirical performance of OCCFM, we conduct extensive experiments on 6 real-world datasets for online recommendation and binary classification tasks. The experimental results show that OCCFM outperforms the state-of-art online learning algorithms.
Deep convolutional neural network has achieved superior recognition performance on many public object detection datasets. However, under the weather conditions of rain or fog, the scarcity of samples has always been the problems restricting the accuracy of detection and identification. To solve this problem, this paper proposed an object detection method for heavy fog scenes based on image defogging and sample enhancement. Firstly, generative adversarial network (GAN) is adopted to remove the fog from images, and then achieve sample enhancement by a style transfer network, which keeps the image content basically unchanged and transform the style of image texture. Fog-free dataset after sample enhancement can reduce the influence of the texture information on the network model and make it pay more attention to the contour information of the object shape. The experimental results on I-HAZE and REISDE dataset show that our proposed method can effectively improve the object detection precision and the mAP (mean average precision) can be improved by up to 15%.
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